Results 151 to 160 of about 142,623 (210)

Predicting and explaining poor prognosis in diabetic kidney disease using SHAP-based interpretable machine learning. [PDF]

iScience
Qian M, Li L, Cheng Y, Hou Y, Fu S, Xu Z.   +5 more
europepmc   +1 more source

Machine Learning-Based Accurate Full-Sib Family Assignment in Sturgeon Using Whole-Genome Sequencing Data. [PDF]

Int J Mol Sci
Yan J, Li H, Dong T, Wang W, Yan X, Bai S, Song H, Hu H.   +7 more
europepmc   +1 more source

Predicting Anxiety in Individuals with Diabetes: A Comparative Analysis of Machine Learning Algorithms. [PDF]

Probl Endokrinol (Mosk)
Bourkhime H, Qarmiche N, Benmaamar S, Lazar N, Omari M, Berraho M, Tachfouti N, El Fakir S, El Ouahabi H, Otmani N.   +9 more
europepmc   +1 more source

Development and validation of a machine learning-based risk prediction model for cancer-related fatigue in ovarian cancer patients. [PDF]

Front Oncol
Feng R, Fan Z, Pang Y, Ding Q, Yue Q, Wei S.   +5 more
europepmc   +1 more source

Machine learning-based early prediction of multiple chronic disease risk in aging Chinese population: A longitudinal analysis using CHARLS data. [PDF]

Medicine (Baltimore)
Wang Y.
europepmc   +1 more source

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MS-SVM: Minimally Spanned Support Vector Machine

Applied Soft Computing Journal, 2018
Abstract For a Support Vector Machine (SVM) algorithm, the time required for classifying an unknown data point is proportional to the number of support vectors. For some real time applications, use of SVM could be a problem if the number of support vectors is high.
Nikhil R Pal
exaly   +2 more sources

Optimization of support vector machine (SVM) for object classification [PDF]

SPIE Proceedings, 2012
The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used ...
Matthew Scholten, Neil Dhingra, Thomas T. Lu, Tien-Hsin Chao   +3 more
openaire   +2 more sources

If-SVM: Iterative factoring support vector machine

Multimedia Tools and Applications, 2020
Support Vector Machine (SVM) is widely applied in classification and regression tasks where support vectors are pursued through convex quadratic programming technique due to its effectiveness and efficiency. However, existing studies ignore the importance of training samples when they are fed into the model.
Yuqing Pan, Wenpeng Zhai, Wei Gao, Xiangjun Shen   +3 more
openaire   +1 more source